Practical Significance of Effects from Growth Modeling of Alcohol Use Data

酒精使用数据增长模型影响的实际意义

• 批准号: 9311360
• 负责人: Alan J. Feingold
• 金额: $ 31.46万
• 依托单位: OREGON SOCIAL LEARNING CENTER, INC.
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2020-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9311360
• 关键词: Abstinence; Address; Adolescence; Adolescent; Alcohol abuse; Alcohol consumption; Alcohol or Other Drugs use; Alcohols; Behavior; Big Data; Big Data to Knowledge; Biological; Brain; Categories; Communities; Confidence Intervals; Data; Data Analyses; Dependence; Development; Disease; Drug abuse; Epidemiology; Equation; Ethnic Origin; Etiology; Family; Foundations; Gender; Generations; Goals; Growth; Growth Factor; Health; Individual; Intercept; Light; Literature; Longitudinal Studies; Measures; Mediation; Mediator of activation protein; Meta-Analysis; Methodology; Methods; Modeling; National Institute of Drug Abuse; National Institute on Alcohol Abuse and Alcoholism; National Longitudinal Survey of Youth; Nature; Odds Ratio; Outcome; Participant; Pathway interactions; Pattern; Pharmaceutical Preparations; Predictive Factor; Prevention; Prospective Studies; Publishing; Randomized Clinical Trials; Research; Research Personnel; Risk; Risk Factors; Sample Size; Sampling; Sex Characteristics; Standardization; Statistical Data Interpretation; Substance abuse problem; Teenagers; Testing; Time; United States National Institutes of Health; Woman; Work; addiction; alcohol abuse prevention; alcohol misuse; alcohol use disorder; base; binge drinking; cognitive development; cohort; cost; design; drinking; emerging adult; gender difference; interest; longitudinal analysis; men; mortality; neurodevelopment; novel strategies; secondary analysis; statistics; time interval; treatment effect

Abstract Longitudinal research on alcohol consumption, alcohol use disorders, and heavy episodic drinking often uses latent growth modeling analysis (GMA) and growth mixture modeling (GMM) to examine pathways to alcohol and drug abuse (including the risk and protective factors that predict them) or health consequences from abuse. Such studies of substance abuse trajectories are useful for understanding the etiology of alcohol and drug problems, and for informing efforts for both prevention and treatment. Examples of such prospective studies, both of which target adolescents, include NIAAA's National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) Study and NIDA's Adolescent Brain Cognitive Development (ABCD) Study. Both of these studies are also examples of big data—a current focus of NIH (e.g., their BD2K initiative)—in that they use hundreds or thousands of participants and are ideally suited for analyses with the GMA approach (which requires large samples). However, in spite of the widely recognized need for effect sizes and their confidence intervals (CIs) in statistical analyses, development of such statistics for findings from GMA and GMM has been limited. In our prior work, we have proposed a regression framework for effect size assessments and formulated an equation for a standardized effect size for GMA that transforms the trajectory difference into a standardized mean difference in the metric of Cohen's d, which is now widely used in the literature on randomized clinical trials (RCTs). We have also developed formulas for estimating the CI for the GMA effect size. We will first use that model to develop new statistics that fill critical gaps in our published work regarding GMA effect sizes and related statistics (e.g., SEs and CIs) for RCTs, which will provide a foundation for subsequent aims. Next, we will introduce new kinds of effect sizes for direct and indirect effects that would be useful to both GMA and non-GMA researchers examining mediation, although our secondary analyses that illustrate our methods will involve mediation in GMA. The proposed work will formulate equations for new effect sizes for GMA and GMM in different metrics, and Monte Carlo studies will be conducted to examine biases (and determine necessary sample sizes) for point estimates and CIs for the effect sizes for different types of GMA and GMM hypothesis tests commonly found in the literature. In addition, we will conduct a secondary analysis study of alcohol use data from both the current and previous National Longitudinal Surveys of Youth to examine whether, as predicted, the effects of gender and on growth, persistence, and desistance of alcohol use from the teen years into early adulthood has decreased between cohorts born 20 years apart—and to attempt to identify mediators responsible for the expected changes in effect sizes and whether mediation is moderated by other factors (e.g., ethnicity). Thus, this project would also illustrate the use of our new statistics in meta-analysis. Most important, we will publish tutorial articles to widely disseminate out derived equations for communicating the practical significance of findings from GMA and GMM.

摘要 关于酒精消费、酒精使用障碍和间歇性重度饮酒的纵向研究经常使用 潜在增长模型分析（GMA）和增长混合模型（GMM），以检查酒精的途径 和药物滥用（包括预测它们的风险和保护因素）或 虐待这种物质滥用轨迹的研究有助于了解酒精的病因， 毒品问题，并为预防和治疗工作提供信息。这种前景的例子 研究，这两个目标青少年，包括NIAAA的国家酒精联盟和 青少年神经发育（NCANDA）研究和NIDA的青少年大脑认知发育 （ABCD）学习。这两项研究也是大数据的例子-NIH目前的重点（例如，BD2K 倡议）-因为它们使用数百或数千名参与者，并且非常适合使用 全球海洋环境状况评估方法（需要大样本）。然而，尽管广泛认识到需要效应大小， 及其统计分析中的置信区间，为全球海洋环境状况评估的结果编制此类统计数据 而GMM是有限的。在我们之前的工作中，我们提出了一个回归框架的效果大小 评估，并制定了一个方程的标准化效应大小的全球海洋环境状况评估， 差异转化为科恩d度量中的标准化平均差异，科恩d度量现在广泛用于 随机临床试验（RCT）的文献。我们还开发了估算CI的公式， GMA效应量。我们将首先使用该模型开发新的统计数据，以填补我们已发表工作中的关键空白 关于GMA效应大小和相关统计（例如，SE和CI），这将为RCT提供基础 为了以后的目标。接下来，我们将为直接和间接效应引入新的效应量， 对研究调解的GMA和非GMA研究人员都有用，尽管我们的次要分析 说明我们方法将涉及全球海洋环境状况评估中的调解。拟议的工作将制定新的影响方程 在不同的度量中，GMA和GMM的大小，并将进行蒙特卡罗研究以检查偏差 (and确定必要的样本量）的点估计值和不同类型的效应量的CI GMA和GMM假设检验常见于文献中。此外，我们将进行第二次 对当前和以前的全国青年纵向调查中的酒精使用数据进行分析研究 研究是否如预测的那样，性别和对生长，持久性和戒酒的影响 从青少年到成年早期的使用在出生间隔20年的队列中有所减少， 试图确定负责效应量预期变化的调解人，以及调解是否 由其它因素调节（例如，种族）。因此，这个项目也将说明我们的新统计数据的使用情况 进行荟萃分析。最重要的是，我们将发表教程文章，广泛传播导出的方程 交流全球海洋环境状况评估和全球海洋监测结果的实际意义。